Coercing machine learning to output physically accurate results

DOI10.1016/j.jcp.2019.109099zbMath1453.68164arXiv1910.09671OpenAlexW2985010153MaRDI QIDQ2223280

Yanyan Li

Publication date: 28 January 2021

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.09671

zbMATH Keywords

machine learningphysical simulation

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Physics (00A79)

Related Items (6)

Symplectic neural networks in Taylor series form for Hamiltonian systems ⋮ Solving inverse-PDE problems with physics-aware neural networks ⋮ On obtaining sparse semantic solutions for inverse problems, control, and neural network training ⋮ Neural Networks with Local Converging Inputs (NNLCI) for Solving Conservation Laws, Part I: 1D Problems ⋮ Addressing discontinuous root-finding for subsequent differentiability in machine learning, inverse problems, and control ⋮ Convex optimization with an interpolation-based projection and its application to deep learning

Uses Software

• L-BFGS
• ElemStatLearn
• ECOS
• ImageNet
• AdaGrad
• RMSprop
• Adam
• DGM
• ADADELTA
• OptNet

Cites Work

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